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We study the problem of safe and intention-aware robot navigation in denseand interactive crowds. Most previous reinforcement learning (RL) based methodsfail to consider different types of interactions among all agents or ignore theintentions of people, which results in performance degradation. To learn a safeand efficient robot policy, we propose a novel recurrent graph neural networkwith attention mechanisms to capture heterogeneous interactions among agentsthrough space and time. To encourage longsighted robot behaviors, we infer theintentions of dynamic agents by predicting their future trajectories forseveral timesteps. The predictions are incorporated into a model-free RLframework to prevent the robot from intruding into the intended paths of otheragents. We demonstrate that our method enables the robot to achieve goodnavigation performance and non-invasiveness in challenging crowd navigationscenarios. We successfully transfer the policy learned in simulation to areal-world TurtleBot 2i. Our code and videos are available athttps://sites.google.com/view/intention-aware-crowdnav/home.

Intention Aware Robot Crowd Navigation with Attention-Based Interaction Graph